{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Concept 2: Dynamical System\n",
    "\n",
    "[![Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/brainpy/brainpy/blob/master/docs_version2/core_concept/brainpy_dynamical_system.ipynb)\n",
    "[![Open in Kaggle](https://kaggle.com/static/images/open-in-kaggle.svg)](https://kaggle.com/kernels/welcome?src=https://github.com/brainpy/brainpy/blob/master/docs_version2/core_concept/brainpy_dynamical_system.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "@[Chaoming Wang](https://github.com/chaoming0625)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "BrainPy supports modelings in brain simulation and brain-inspired computing.\n",
    "\n",
    "All these supports are based on one common concept: **Dynamical System** via ``brainpy.DynamicalSystem``.\n",
    "\n",
    "Therefore, it is essential to understand:\n",
    "1. what is ``brainpy.DynamicalSystem``?\n",
    "2. how to define ``brainpy.DynamicalSystem``?\n",
    "3. how to run ``brainpy.DynamicalSystem``?"
   ]
  },
  {
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     "end_time": "2025-10-06T02:55:34.560940Z",
     "start_time": "2025-10-06T02:55:30.658833Z"
    }
   },
   "source": [
    "import brainpy as bp\n",
    "import brainpy.math as bm\n",
    "\n",
    "bm.set_platform('cpu')\n",
    "\n",
    "bp.__version__"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.0.0'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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    }
   ],
   "execution_count": 1
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is ``DynamicalSystem``?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All models used in brain simulation and brain-inspired computing is ``DynamicalSystem``.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "A ``DynamicalSystem`` defines the updating rule of the model at single time step.\n",
    "\n",
    "1. For models with state, ``DynamicalSystem`` defines the state transition from $t$ to $t+dt$, i.e., $S(t+dt) = F\\left(S(t), x, t, dt\\right)$, where $S$ is the state, $x$ is input, $t$ is the time, and $dt$ is the time step. This is the case for recurrent neural networks (like GRU, LSTM), neuron models (like HH, LIF), or synapse models which are widely used in brain simulation.\n",
    "\n",
    "2. However, for models in deep learning, like convolution and fully-connected linear layers, ``DynamicalSystem`` defines the input-to-output mapping, i.e., $y=F\\left(x, t\\right)$."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](imgs/dynamical_system.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to define ``DynamicalSystem``?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Keep in mind that the usage of ``DynamicalSystem`` has several constraints in BrainPy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. ``.update()`` function\n",
    "\n",
    "First, *all ``DynamicalSystem`` should implement ``.update()`` function*, which receives two arguments:\n",
    "\n",
    "```\n",
    "class YourModel(bp.DynamicalSystem):\n",
    "  def update(self, s, x):\n",
    "    pass\n",
    "```\n",
    "\n",
    "- `s` (or named as others): A dict, to indicate shared arguments across all nodes/layers in the network, like\n",
    "    - the current time ``t``, or\n",
    "    - the current running index ``i``, or\n",
    "    - the current time step ``dt``, or\n",
    "    - the current phase of training or testing ``fit=True/False``.\n",
    "- `x` (or named as others): The individual input for this node/layer.\n",
    "\n",
    "We call `s` as shared arguments because they are same and shared for all nodes/layers. On the contrary, different nodes/layers have different input `x`.\n",
    "\n",
    "Here, it is necessary to explain the usage of ``bp.share``.\n",
    "- ``bp.share.save( )``: The function saves shared arguments in the global context. User can save shared arguments in two ways, for example, if user want to set the current time ``t=100``, the current time step ``dt=0.1``,the user can use ``bp.share.save(\"t\",100,\"dt\",0.1)`` or                      ``bp.share.save(t=100,dt=0.1)``.\n",
    "  \n",
    "- ``bp.share.load( )``: The function gets the shared data by the ``key``, for example, ``bp.share.load(\"t\")``.\n",
    "  \n",
    "- ``bp.share.clear_shargs( )``: The function clears the specific shared arguments in the global context, for example, ``bp.share.clear_shargs(\"t\")``.\n",
    "  \n",
    "- ``bp.share.clear( )``: The function clears all shared arguments in the global context.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example: LIF neuron model for brain simulation**\n",
    "\n",
    "Here we illustrate the first constraint of ``DynamicalSystem`` using the Leaky Integrate-and-Fire (LIF) model.\n",
    "\n",
    "The LIF model is firstly proposed in brain simulation for modeling neuron dynamics. Its equation is given by\n",
    "\n",
    "$$\n",
    "\\begin{aligned}\n",
    "\\tau_m \\frac{dV}{dt} = - (V(t) - V_{rest}) + I(t)  \\\\\n",
    "\\text{if} \\, V(t) \\gt V_{th}, V(t) =V_{rest}\n",
    "\\end{aligned}\n",
    "$$\n",
    "\n",
    "For the details of the model, users should refer to Wikipedia or other resource.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T02:55:34.573484Z",
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   },
   "source": [
    "class LIF_for_BrainSimulation(bp.DynamicalSystem):\n",
    "    def __init__(self, size, V_rest=0., V_th=1., tau=5., mode=None):\n",
    "        super().__init__(mode=mode)\n",
    "\n",
    "        # this model only supports non-batching mode\n",
    "        bp.check.is_subclass(self.mode, bm.NonBatchingMode)\n",
    "\n",
    "        # parameters\n",
    "        self.size = size\n",
    "        self.V_rest = V_rest\n",
    "        self.V_th = V_th\n",
    "        self.tau = tau\n",
    "\n",
    "        # variables\n",
    "        self.V = bm.Variable(bm.ones(size) * V_rest)\n",
    "        self.spike = bm.Variable(bm.zeros(size, dtype=bool))\n",
    "\n",
    "        # integrate differential equation with exponential euler method\n",
    "        self.integral = bp.odeint(f=lambda V, t, I: (-V + V_rest + I) / tau, method='exp_auto')\n",
    "\n",
    "    def update(self, x):\n",
    "        # define how the model states update\n",
    "        # according to the external input\n",
    "        t = bp.share.load('t')\n",
    "        dt = bp.share.load('dt')\n",
    "        V = self.integral(self.V, t, x, dt=dt)\n",
    "        spike = V >= self.V_th\n",
    "        self.V.value = bm.where(spike, self.V_rest, V)\n",
    "        self.spike.value = spike\n",
    "        return spike"
   ],
   "outputs": [],
   "execution_count": 2
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Computing mode\n",
    "\n",
    "Second, **explicitly consider which computing mode your ``DynamicalSystem`` supports**.\n",
    "\n",
    "Brain simulation usually builds models without batching dimension (we refer to it as *non-batching mode*, as seen in above LIF model), while brain-inspired computation trains models with a batch of data (*batching mode* or *training mode*).\n",
    "\n",
    "So, to write a model applicable to abroad applications in brain simulation and brain-inspired computing, you need to consider which mode your model supports, one of them, or both of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Example: LIF neuron model for both brain simulation and brain-inspired computing**\n",
    "\n",
    "When considering the computing mode, we can program a general LIF model for brain simulation and brain-inspired computing.\n",
    "\n",
    "To overcome the non-differential property of the spike in the LIF model for brain simulation, i.e., at the code of\n",
    "\n",
    "```python\n",
    "spike = V >= self.V_th\n",
    "```\n",
    "\n",
    "LIF models used in brain-inspired computing calculate the spiking state using the surrogate gradient function. Usually, we replace the backward gradient of the spike with a smooth function, like\n",
    "\n",
    "$$\n",
    "g'(x) = \\frac{1}{(\\alpha * |x| + 1.) ^ 2}\n",
    "$$"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
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     "end_time": "2025-10-06T02:55:34.588451Z",
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   },
   "source": [
    "class LIF(bp.DynamicalSystem):\n",
    "    supported_modes = (bm.NonBatchingMode, bm.BatchingMode, bm.TrainingMode)\n",
    "\n",
    "    def __init__(self, size, f_surrogate=None, V_rest=0., V_th=1., tau=5., mode=None):\n",
    "        super().__init__(mode=mode)\n",
    "\n",
    "        # Parameters\n",
    "        self.size = size\n",
    "        self.num = bp.tools.size2num(size)\n",
    "        self.V_rest = V_rest\n",
    "        self.V_th = V_th\n",
    "        self.tau = tau\n",
    "        if f_surrogate is None:\n",
    "            f_surrogate = bm.surrogate.inv_square_grad\n",
    "        self.f_surrogate = f_surrogate\n",
    "\n",
    "        # integrate differential equation with exponential euler method\n",
    "        self.integral = bp.odeint(f=lambda V, t, I: (-V + V_rest + I) / tau, method='exp_auto')\n",
    "\n",
    "        # Initialize a Variable:\n",
    "        # - if non-batching mode, batch axis of V is None\n",
    "        # - if batching mode,     batch axis of V is 0\n",
    "        self.V = bp.init.variable_(bm.zeros, self.size, self.mode)\n",
    "        self.V[:] = self.V_rest\n",
    "        self.spike = bp.init.variable_(bm.zeros, self.size, self.mode)\n",
    "\n",
    "    def reset_state(self, batch_size=None):\n",
    "        self.V.value = bp.init.variable_(bm.ones, self.size, batch_size) * self.V_rest\n",
    "        self.spike.value = bp.init.variable_(bm.zeros, self.size, batch_size)\n",
    "\n",
    "    def update(self, x):\n",
    "        t = bp.share.load('t')\n",
    "        dt = bp.share.load('dt')\n",
    "        V = self.integral(self.V, t, x, dt=dt)\n",
    "        # replace non-differential heaviside function\n",
    "        # with a surrogate gradient function\n",
    "        spike = self.f_surrogate(V - self.V_th)\n",
    "        # reset membrane potential\n",
    "        self.V.value = (1. - spike) * V + spike * self.V_rest\n",
    "        self.spike.value = spike\n",
    "        return spike"
   ],
   "outputs": [],
   "execution_count": 3
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model composition\n",
    "\n",
    "The ``LIF`` model we have defined above can be recursively composed to construct networks in brain simulation and brain-inspired computing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code snippet utilizes the LIF model to build an E/I balanced network ``EINet``, which is a classical network model in brain simulation."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T02:55:40.548106Z",
     "start_time": "2025-10-06T02:55:34.593483Z"
    }
   },
   "source": [
    "class Exponential(bp.Projection):\n",
    "    def __init__(self, num_pre, post, prob, g_max, tau):\n",
    "        super(Exponential, self).__init__()\n",
    "        self.proj = bp.dyn.ProjAlignPostMg1(\n",
    "            comm=bp.dnn.EventCSRLinear(bp.conn.FixedProb(prob, pre=num_pre, post=post.num), g_max),\n",
    "            syn=bp.dyn.Expon.desc(post.num, tau=tau),\n",
    "            out=bp.dyn.CUBA.desc(),\n",
    "            post=post\n",
    "        )\n",
    "\n",
    "    def update(self, x):\n",
    "        return self.proj(x)\n",
    "\n",
    "\n",
    "class EINet(bp.DynamicalSystem):\n",
    "    def __init__(self, num_exc, num_inh):\n",
    "        super().__init__()\n",
    "        self.E = LIF(num_exc, V_rest=-55, V_th=-50., tau=20.)\n",
    "        self.I = LIF(num_inh, V_rest=-55, V_th=-50., tau=20.)\n",
    "        self.E2E = Exponential(prob=0.02, num_pre=num_exc, post=self.E, g_max=1.62, tau=5.)\n",
    "        self.E2I = Exponential(prob=0.02, num_pre=num_exc, post=self.I, g_max=1.62, tau=5.)\n",
    "        self.I2E = Exponential(prob=0.02, num_pre=num_inh, post=self.E, g_max=-9.0, tau=10.)\n",
    "        self.I2I = Exponential(prob=0.02, num_pre=num_inh, post=self.I, g_max=-9.0, tau=10.)\n",
    "\n",
    "    def update(self, x):\n",
    "        # x is the background input\n",
    "        e2e = self.E2E(self.E.spike)\n",
    "        e2i = self.E2I(self.E.spike)\n",
    "        i2e = self.I2E(self.I.spike)\n",
    "        i2i = self.I2I(self.I.spike)\n",
    "        self.E(e2e + i2e + x)\n",
    "        self.I(e2i + i2i + x)\n",
    "\n",
    "\n",
    "with bm.environment(mode=bm.nonbatching_mode):\n",
    "    net1 = EINet(3200, 800)"
   ],
   "outputs": [],
   "execution_count": 4
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Moreover, our LIF model can also be used in brain-inspired computing scenario. The following ``AINet`` uses the LIF model to construct a model for AI training."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T02:55:43.313223Z",
     "start_time": "2025-10-06T02:55:42.724747Z"
    }
   },
   "source": [
    "# This network can be used in AI applications\n",
    "\n",
    "class AINet(bp.DynamicalSystem):\n",
    "    def __init__(self, sizes):\n",
    "        super().__init__()\n",
    "        self.neu1 = LIF(sizes[0])\n",
    "        self.syn1 = bp.dnn.Dense(sizes[0], sizes[1])\n",
    "        self.neu2 = LIF(sizes[1])\n",
    "        self.syn2 = bp.dnn.Dense(sizes[1], sizes[2])\n",
    "        self.neu3 = LIF(sizes[2])\n",
    "\n",
    "    def update(self, x):\n",
    "        return x >> self.neu1 >> self.syn1 >> self.neu2 >> self.syn2 >> self.neu3\n",
    "\n",
    "\n",
    "with bm.environment(mode=bm.training_mode):\n",
    "    net2 = AINet([100, 50, 10])"
   ],
   "outputs": [],
   "execution_count": 5
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## How to run ``DynamicalSystem``?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we have stated above that ``DynamicalSystem`` only defines the updating rule at single time step, to run a ``DynamicalSystem`` instance over time, we need a for loop mechanism.\n",
    "\n",
    "![](./imgs/dynamical_system_and_dsrunner.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. ``brainpy.math.for_loop``\n",
    "\n",
    "``for_loop`` is a structural control flow API which runs a function with the looping over the inputs. Moreover, this API just-in-time compile the looping process into the machine code.\n",
    "\n",
    "Suppose we have 200 time steps with the step size of 0.1, we can run the model with:"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
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     "end_time": "2025-10-06T02:55:43.321877Z",
     "start_time": "2025-10-06T02:55:43.318468Z"
    }
   },
   "source": [
    "def run_net2(t, currents):\n",
    "    bp.share.save(t=t)\n",
    "    return net2(currents)"
   ],
   "outputs": [],
   "execution_count": 6
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T02:55:43.988694Z",
     "start_time": "2025-10-06T02:55:43.330959Z"
    }
   },
   "source": [
    "with bm.environment(dt=0.1):\n",
    "    # construct the given time steps\n",
    "    times = bm.arange(200) * bm.dt\n",
    "    # construct the inputs with shape of (time, batch, feature)\n",
    "    currents = bm.random.rand(200, 10, 100)\n",
    "\n",
    "    # run the model\n",
    "    net2.reset(10)\n",
    "    out = bm.for_loop(run_net2, (times, currents))\n",
    "\n",
    "out.shape"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 10, 10)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 7
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. ``brainpy.LoopOverTime``\n",
    "\n",
    "Different from ``for_loop``, ``brainpy.LoopOverTime`` is used for constructing a dynamical system that automatically loops the model over time when receiving an input.\n",
    "\n",
    "``for_loop`` runs the model over time. While ``brainpy.LoopOverTime`` creates a model which will run the model over time when calling it."
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-10-06T02:55:44.248009Z",
     "start_time": "2025-10-06T02:55:44.006284Z"
    }
   },
   "source": [
    "net2.reset(10)\n",
    "looper = bp.LoopOverTime(net2)\n",
    "out = looper(currents)\n",
    "out.shape"
   ],
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(200, 10, 10)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "execution_count": 8
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. ``brainpy.DSRunner``\n",
    "\n",
    "Another way to run the model in BrainPy is using the structural running object ``DSRunner`` and ``DSTrainer``. They provide more flexible way to monitoring the variables in a ``DynamicalSystem``. The details users should refer to the [DSRunner tutorial](../tutorial_simulation/simulation_dsrunner.ipynb).\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "ExecuteTime": {
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     "start_time": "2025-10-06T02:55:44.264477Z"
    }
   },
   "source": [
    "with bm.environment(dt=0.1):\n",
    "    runner = bp.DSRunner(net1, monitors={'E.spike': net1.E.spike, 'I.spike': net1.I.spike})\n",
    "    runner.run(inputs=bm.ones(1000) * 20.)\n",
    "\n",
    "bp.visualize.raster_plot(runner.mon['ts'], runner.mon['E.spike'])"
   ],
   "outputs": [
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"
     },
     "metadata": {},
     "output_type": "display_data",
     "jetTransient": {
      "display_id": null
     }
    }
   ],
   "execution_count": 9
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
